In the first training phase (related to the disease incremental learning), we train the candidate classification model (incrementally) to recognize different chest abnormalities such as effusion, pulmonary edema, pleural thickening, etc., from the first dataset. Here, in each training increment, the \(L_{IL}\)  loss function ensures that the network minimizes the classification loss while learning newly added disease categories. Furthermore, \(L_{IL}\)  also penalizes the classification network to distill its prior learned knowledge representations through a subset of training examples that were used in the previous iteration. Moreover, unlike other incremental learning schemes [1]}, [2]}, [3]}, [4]}, the proposed \(L_{IL}\)  also minimizes the mutual distillation objective function \(L_{MD}\) . \(L_{MD}\)  ensures that the classification model resolves the complex inter-dependencies between incrementally learned knowledge representations via Bayesian inference. More details about the loss functions are presented in Section REF .
